Lindsay Munroe¹, Azhaar Ashraf¹, Antigoni Ekonomou¹, Harold Parkes¹, Claire Troakes¹, and Po-Wah So^1
1Neuro-imaging, King's College London, London, United Kingdom

Brain iron dyshomeostasis/iron overload has been observed in Alzheimer's disease (AD). Being sensitive to the presence of iron, magnetic resonance imaging (MRI), is often used to study the role of iron in disease. In this study, we determined group differences in iron, R2^* and myelin, acquired from the same ex-vivo mid-temporal gyri samples from AD and cognitively normal (CN) subjects. We observed decreased myelin and iron in white matter AD compared to CN. These white matter changes are likely due to a loss of iron-rich oligodendrocytes and demyelination during AD pathogenesis.

Courtney J Comrie¹, Laurel A Dieckhaus¹, Tom G Beach², Geidy E Serrano², and Elizabeth B Hutchinson^1
1Biomedical Engineering, University of Arizona, Tucson, AZ, United States, ²Brain and Body Donation Program, Banner Sun Health Research Institute, Sun City, AZ, United States

Alzheimer’s is an irreversible degenerative brain disease. Current clinical MRI is capable of reporting severe brain atrophy, but fails to recognize earlier biomarkers associated with more subtle microstructural changes. Microstructural MRI techniques such as DTI, MAP-MRI, NODDI, MWF, and BPF are promising to address this challenge and may sensitively detect and distinguish tissue degeneration, tauopathies, and beta amyloid plaques. The capability of these techniques was investigated in post-mortem human temporal lobe specimens at high resolution and high image quality. Prominent findings seen were distinct differences between relaxivity and diffusivity metrics, and striking differences between DTI and MAP-MRI anisotropy metrics.

Christian Tinauer¹, Lukas Pirpamer¹, Marc Masana², Stefan Heber¹, Anna Damulina¹, Maximilian Sackl¹, Martin Soellradl³, Reinhold Schmidt¹, Stefan Ropele¹, and Christian Langkammer^1
1Department of Neurology, Medical University of Graz, Graz, Austria, ²Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria, ³Institute of Medical Engineering, Graz University of Technology, Graz, Austria

Increased iron deposition in the basal ganglia is a frequent finding in patients with AD. Using R2* maps we separated Alzheimer's patients (n=115) from healthy controls (n=169) by using a deep neural network and systematically investigated the influence of the learned features using an attached relevance map generator. The highest relevances were found in and adjacent to the basal ganglia, which is in line with established histological findings and additionally confirmed by the conventional ROI-based analysis. This study demonstrates the validity of heat mapping as a means to identify novel areas of pathological tissue changes.

Jinseong Jang¹ and Dosik Hwang^1
1Yonsei University, Seoul, Korea, Republic of

We propose Transformer-based Alzheimer’s disease (AD) analyzer for 3D MRI. The proposed network can analyze 3D MRI images efficiently combining 3D CNN, and Transformer. It is possible to efficiently extract locality information for AD-related abnormalities in local brain based on CNN networks with inductive bias. Also, the transformer network is also used to obtain attention relationship among 3D representation features after CNN. Our proposed method was compared to various networks including 3D CNN and transformer with an area under curve  and accuracy for AD classification in multi-institutional datasets. Also, the transformer interpretability technique-based activation map can visualize AD-related abnormality region.

Luigi Lorenzini¹, Ingala Silvia¹, Lyduine E Collij¹, Betty Tijms², Henk JMM Mutsaerts ^1,3, Viktor Wottschel ¹, Sven Haller^4,5, Kaj Blennow ^6,7, Giovanni Frisoni^8,9, Gael Chételat ¹⁰, Pierre Payoux ^11,12, Pablo Lage-Martinez ¹³, Adam Waldman ^14,15, Joanna Wardlaw ^14,16, Craig Ritchie¹⁷, Juan Domingo Gispert ^18,19,20,21, Pieter Jelle Visser ^2,22,23, Philip Scheltens ², Barkhof Frederik¹, and Alle Meije Wink ^1
1Dept. of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Vrije Universiteit, Amsterdam Neuroscience, Amsterdam, The Netherlands, Amsterdam, Netherlands, ²Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands, Amsterdam, Netherlands, ³Ghent Institute for Functional and Metabolic Imaging (GIfMI), Ghent University, Ghent, Belgium, Ghent, Belgium, ⁴CIRD Centre d’Imagerie Rive Droite, Geneva, Switzerland, Geneva, Switzerland, ⁵Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden, Uppsala, Sweden, ⁶Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Sweden, Gothenburg, Sweden, ⁷Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden, Mölndal, Sweden, ⁸aboratory Alzheimer’s Neuroimaging & Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy, Brescia, Italy, ⁹University Hospitals and University of Geneva, Geneva, Switzerland, Geneva, Switzerland, ¹⁰Université de Normandie, Unicaen, Inserm, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", institut Blood-and-Brain @ Caen-Normandie, Cyceron, 14000 Caen, France, Caen, France, ¹¹Department of Nuclear Medicine, Toulouse CHU, Purpan University Hospital, Toulouse, France, Toulouse, France, ¹²Toulouse NeuroImaging Center, University of Toulouse, INSERM, UPS, Toulouse, France, Toulouse, France, ¹³Centro de Investigación y Terapias Avanzadas, Neurología, CITA‐Alzheimer Foundation, San Sebastián, Spain, San Sebastián, Spain, ¹⁴Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK, Edinburgh, Scotland, ¹⁵Department of Medicine, Imperial College London, London, UK, London, United Kingdom, ¹⁶UK Dementia Research Institute at Edinburgh, University of Edinburgh, UK, Edinburgh, Scotland, ¹⁷Centre for Dementia Prevention, The University of Edinburgh, Scotland, UK, Edinburgh, Scotland, ¹⁸Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain, Barcelona, Spain, ¹⁹CIBER Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain, Madrid, Spain, ²⁰IMIM (Hospital del Mar Medical Research Institute), Barcelona Spain, Barcelona, Spain, ²¹Universitat Pompeu Fabra, Barcelona, Spain, Barcelona, Spain, ²²Alzheimer Center Limburg, Department of Psychiatry & Neuropsychology, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands, Maastricht, Netherlands, ²³Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden, Stockholm, Sweden

Recent evidence suggests that amyloid deposition in the brain follows — and subsequently affects — temporal variability of neuronal activity and functional connectivity. Temporal variability in functional brain network properties connote changes in participation of functional hubs in different subnetworks. We confirm previous findings of alterations of functional eigenvector centrality (EC) in relation to amyloid, and demonstrate that those regions show altered dynamic EC profiles with lower variability over time, hence lower network integration.

Anita Monteverdi^1,2, Fulvia Palesi², Claudia AM Gandini Wheeler-Kingshott^1,2,3, and Egidio D'Angelo^1,2
1Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy, ²Brain and Behavioural Sciences, University of Pavia, Pavia, Italy, ³NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, UCL, London, United Kingdom

The Virtual Brain (TVB) is a novel modeling platform to simulate whole-brain dynamics starting from individual structural and functional connectivity. In this work, we performed TVB simulations in neurodegenerative diseases (i.e. Alzheimer’s disease, Frontotemporal Dementia, and Amyotrophic Lateral Sclerosis) providing a unique description of the excitatory/inhibitory balance at single-subject level. TVB-derived biophysical parameters were different among clinical phenotypes, presented an association with neuropsychological domains and improved discriminative power between clinical conditions. Overall, this work suggests a novel TVB-based approach for future personalized diagnosis and therapy in neurodegenerative diseases, providing a subject-specific description of excitation/inhibition profiles.